Enhanced Parameter Estimation of Solar Photovoltaic Models Using QLESCA Algorithm

Qusay Shihab Hamad; Sami Abdulla Mohsen Saleh; Shahrel Azmin Suandi; Hussein Samma; Yasameen Shihab Hamad; Imran Riaz

doi:10.1007/978-981-99-9005-4_25

Enhanced Parameter Estimation of Solar Photovoltaic Models Using QLESCA Algorithm

Qusay Shihab Hamad
, Sami Abdulla Mohsen Saleh
, Shahrel Azmin Suandi^*
, Hussein Samma
, Yasameen Shihab Hamad
, Imran Riaz

^*Corresponding author for this work

SDAIA-KFUPM Joint Research Center for Artificial Intelligence

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Photovoltaic (PV) systems are recognized as an important section in the utilization of solar power, and the optimisation, control, and mockup of these systems are of great significance. However, the performance of PV systems is mainly motivated by model constraints that are varying and often absent, making their accurate and robust estimation a challenge for existing methods. In this study, the effect of using the Q-learning embedded sine cosine algorithm (QLESCA) in the selection of optimal PV model parameters is investigated. The performance of QLESCA is evaluated and compared with other optimizers. The results show that QLESCA achieves higher efficiency in accurately estimating PV model parameters. This research provides an efficient and effective method for identifying optimal PV model parameters and contributes to the field of PV system optimization, control, and simulation.

Original language	English
Title of host publication	Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications
Editors	Nur Syazreen Ahmad, Junita Mohamad-Saleh, Jiashen Teh
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	199-205
Number of pages	7
ISBN (Print)	9789819990047
DOIs	https://doi.org/10.1007/978-981-99-9005-4_25
State	Published - 2024
Event	12th International Conference on Robotics, Vision, Signal Processing, and Power Applications, ROVISP 2023 - Penang, Malaysia Duration: 28 Aug 2023 → 29 Aug 2023

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	1123 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	12th International Conference on Robotics, Vision, Signal Processing, and Power Applications, ROVISP 2023
Country/Territory	Malaysia
City	Penang
Period	28/08/23 → 29/08/23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Parameter Estimation
Photovoltaic models
QLESCA
Sine cosine algorithm
Solar photovoltaic system

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

Access to Document

10.1007/978-981-99-9005-4_25

Cite this

Hamad, Q. S., Saleh, S. A. M., Suandi, S. A., Samma, H., Hamad, Y. S., & Riaz, I. (2024). Enhanced Parameter Estimation of Solar Photovoltaic Models Using QLESCA Algorithm. In N. S. Ahmad, J. Mohamad-Saleh, & J. Teh (Eds.), Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications (pp. 199-205). (Lecture Notes in Electrical Engineering; Vol. 1123 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-9005-4_25

Hamad, Qusay Shihab ; Saleh, Sami Abdulla Mohsen ; Suandi, Shahrel Azmin et al. / Enhanced Parameter Estimation of Solar Photovoltaic Models Using QLESCA Algorithm. Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications. editor / Nur Syazreen Ahmad ; Junita Mohamad-Saleh ; Jiashen Teh. Springer Science and Business Media Deutschland GmbH, 2024. pp. 199-205 (Lecture Notes in Electrical Engineering).

@inproceedings{ee1614f9c46b419e841894c2596096b1,

title = "Enhanced Parameter Estimation of Solar Photovoltaic Models Using QLESCA Algorithm",

abstract = "Photovoltaic (PV) systems are recognized as an important section in the utilization of solar power, and the optimisation, control, and mockup of these systems are of great significance. However, the performance of PV systems is mainly motivated by model constraints that are varying and often absent, making their accurate and robust estimation a challenge for existing methods. In this study, the effect of using the Q-learning embedded sine cosine algorithm (QLESCA) in the selection of optimal PV model parameters is investigated. The performance of QLESCA is evaluated and compared with other optimizers. The results show that QLESCA achieves higher efficiency in accurately estimating PV model parameters. This research provides an efficient and effective method for identifying optimal PV model parameters and contributes to the field of PV system optimization, control, and simulation.",

keywords = "Parameter Estimation, Photovoltaic models, QLESCA, Sine cosine algorithm, Solar photovoltaic system",

author = "Hamad, \{Qusay Shihab\} and Saleh, \{Sami Abdulla Mohsen\} and Suandi, \{Shahrel Azmin\} and Hussein Samma and Hamad, \{Yasameen Shihab\} and Imran Riaz",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 12th International Conference on Robotics, Vision, Signal Processing, and Power Applications, ROVISP 2023 ; Conference date: 28-08-2023 Through 29-08-2023",

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Hamad, QS, Saleh, SAM, Suandi, SA, Samma, H, Hamad, YS & Riaz, I 2024, Enhanced Parameter Estimation of Solar Photovoltaic Models Using QLESCA Algorithm. in NS Ahmad, J Mohamad-Saleh & J Teh (eds), Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol. 1123 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 199-205, 12th International Conference on Robotics, Vision, Signal Processing, and Power Applications, ROVISP 2023, Penang, Malaysia, 28/08/23. https://doi.org/10.1007/978-981-99-9005-4_25

Enhanced Parameter Estimation of Solar Photovoltaic Models Using QLESCA Algorithm. / Hamad, Qusay Shihab; Saleh, Sami Abdulla Mohsen; Suandi, Shahrel Azmin et al.
Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications. ed. / Nur Syazreen Ahmad; Junita Mohamad-Saleh; Jiashen Teh. Springer Science and Business Media Deutschland GmbH, 2024. p. 199-205 (Lecture Notes in Electrical Engineering; Vol. 1123 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Enhanced Parameter Estimation of Solar Photovoltaic Models Using QLESCA Algorithm

AU - Hamad, Qusay Shihab

AU - Saleh, Sami Abdulla Mohsen

AU - Suandi, Shahrel Azmin

AU - Samma, Hussein

AU - Hamad, Yasameen Shihab

AU - Riaz, Imran

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

PY - 2024

Y1 - 2024

N2 - Photovoltaic (PV) systems are recognized as an important section in the utilization of solar power, and the optimisation, control, and mockup of these systems are of great significance. However, the performance of PV systems is mainly motivated by model constraints that are varying and often absent, making their accurate and robust estimation a challenge for existing methods. In this study, the effect of using the Q-learning embedded sine cosine algorithm (QLESCA) in the selection of optimal PV model parameters is investigated. The performance of QLESCA is evaluated and compared with other optimizers. The results show that QLESCA achieves higher efficiency in accurately estimating PV model parameters. This research provides an efficient and effective method for identifying optimal PV model parameters and contributes to the field of PV system optimization, control, and simulation.

AB - Photovoltaic (PV) systems are recognized as an important section in the utilization of solar power, and the optimisation, control, and mockup of these systems are of great significance. However, the performance of PV systems is mainly motivated by model constraints that are varying and often absent, making their accurate and robust estimation a challenge for existing methods. In this study, the effect of using the Q-learning embedded sine cosine algorithm (QLESCA) in the selection of optimal PV model parameters is investigated. The performance of QLESCA is evaluated and compared with other optimizers. The results show that QLESCA achieves higher efficiency in accurately estimating PV model parameters. This research provides an efficient and effective method for identifying optimal PV model parameters and contributes to the field of PV system optimization, control, and simulation.

KW - Parameter Estimation

KW - Photovoltaic models

KW - QLESCA

KW - Sine cosine algorithm

KW - Solar photovoltaic system

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DO - 10.1007/978-981-99-9005-4_25

M3 - Conference contribution

AN - SCOPUS:85190393845

SN - 9789819990047

T3 - Lecture Notes in Electrical Engineering

SP - 199

EP - 205

BT - Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications

A2 - Ahmad, Nur Syazreen

A2 - Mohamad-Saleh, Junita

A2 - Teh, Jiashen

PB - Springer Science and Business Media Deutschland GmbH

T2 - 12th International Conference on Robotics, Vision, Signal Processing, and Power Applications, ROVISP 2023

Y2 - 28 August 2023 through 29 August 2023

ER -

Hamad QS, Saleh SAM, Suandi SA, Samma H, Hamad YS, Riaz I. Enhanced Parameter Estimation of Solar Photovoltaic Models Using QLESCA Algorithm. In Ahmad NS, Mohamad-Saleh J, Teh J, editors, Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications. Springer Science and Business Media Deutschland GmbH. 2024. p. 199-205. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-99-9005-4_25

Enhanced Parameter Estimation of Solar Photovoltaic Models Using QLESCA Algorithm

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this